Real-time and Contextual Recommendations with Attention Modelling.

1. Attention-Streams GrégoireBurel, OAK Group, University Of Sheffield ESWC 2010, Heraklion, 30 May 2010

2. Introduction Attention-Streams Attention-streams Recommendations: Contextual and real-time recommendations. Passive recommendations. Modelling Attention streams : Attention streams and existing recommendations. Attention vs. Interests. Modelling attention. Monitoring Attention. Attention based recommendations Demo: Video Conclusions

3. Recommender Systems Contextualizing information and users using cross-domain attention modeling. Recommender System: Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user. Information + Users + Interests

4. Recommender Systems Contextualizing information and users using cross-domain attention modeling. Recommender System: Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user. Information + Users + Interests Attention-Streams Attention-Streams (Real-time and Contextual Recommendations)

5. Attention-Streams RecommendationsContextual and Real-time Recommendations

6. Contextual and Real-time Recommendations Features: Models users interests across networks and communities: Interests are not fragmented. Recommendations matches real-time user interests: Information and user interests evolve rapidly independently of the users common interests. Real-time interests might be linked to FOAF profiles: Real-time interests can be shared between different contexts and application. Contextual ‘bookmarks’: Relevant recommendations might be bookmarked by the user. Content Recommendations: Local events using user location and current interests. Information sources using contextual RSS subscriptions. Real-time information streams given current interests.

7. Contextual and Real-time Recommendations Features: Models users interests across networks and communities: Interests are not fragmented. Recommendations matches real-time user interests: Information and user interests evolve rapidly independently of the users common interests. Real-time interests might be linked to FOAF profiles: Real-time interests can be shared between different contexts and application. Contextual ‘bookmarks’: Relevant recommendations might be bookmarked by the user. Content Recommendations: Local events using user location and current interests. Information sources using contextual RSS subscriptions. Real-time information streams given current interests.

8. Passive Recommendations Desktop Cross-domain Interests Mobile Local events + Information Streams + Contextual RSS

9. Passive Recommendations Recommendations do not require any particular action to be accessed: Users might ignore or access the recommendations without disturbing their current workflow.

10. Modelling Attention-StreamsAttention-Streams and Existing Recommendations

11. Attention-Streams and Existing Recommendations Contextualizing information and users using cross-domain attention modeling. Existing recommendations are fragmented, network specific, community dependent and long-term oriented (Resnick, 1997)

12. Attention-Streams and Existing Recommendations Movies Content Events Products Music People

14. User Activity: (Dragunov, 2005)

15. Work/Leisure.

16. News browsing, Finding a Restaurant…Long-term Interests Contextual ‘Interests’ (Middleton, 2004)

17. Attention vs. Interests Attention Management: Attention models have been designed for dealing with interruption overload (attention management): Attention for information notification (Vertegaal, 2003) (Horvitz et al., 2003). Attention and Information Contextualisation: Attention is currently applied to information presentation.

18. Attention vs. Interests Attention Management: Attention models have been designed for dealing with interruption overload (attention management): Attention for information notification (Vertegaal, 2003) (Horvitz et al., 2003). Attention and Information Contextualisation: Attention is currently applied to information presentation. Attention-Streams

19. Attention vs. Interests Attention models can be used for recommending information: Attention  Interests / Interests  Attention Cross-domain Recommendations: Attention is community independent. Real-time recommendations: Attention is real-time / Interests are not (e.g. Middleton, 2004). Ambient Recommendations: Integration of the recommendations in the user workflow. Passive application. Recommender System: Type of information filteringsystem technique that attempts to recommend information items that are likely to be of interest to the user.

20. Modelling Attention using Attention-Streams Attention Tag: AT = {agent, timestamp, domain, tag, weight (…)} Attention: AT = {agent, timestamp, AT set (…)} Attention Tags Attention

21. Attention Tag Attention is represented using lightweight semantics and weighted tags (APML Ontology). Each web document has corresponding attention tags. Attention-Tags might be linked to FOAF profiles. curio: Document curio: Agent

23. WordNet, PMI, NSS (NGD (Cilibrasi, 2004))...

24. Attention-Range Affinity.

25. Attention-Range Calculation:

27. Attention Based Recommendations Media Extraction Service WKI External Website Attention Tags External Website External Website WKI WKI External Website WKI External Website WKI External Website WKI

28. Demohttp://nebula.dcs.shef.ac.uk/sparks/astreams

29. Conclusions Attention-Streams Recommendations: Contextual and Real-time information recommendations. Real-time interests modelling and sharing. Interests derived from user attention. Ambient recommendations.

30. Conclusions Attention-Streams Recommendations: Contextual and Real-time information recommendations. Real-time interests modelling and sharing. Interests derived from user attention. Ambient recommendations. Future work: More recommendations ! (i.e: Social). Integration with streaming ontologies and models (i.e: Sensor Streams). More Attention bookmarking.